There’s a lot of buzz around AI; seemingly out of nowhere, every software platform and tech company has its own AI capabilities.
Is it just tech hype – or is there actual hope that AI will help humans be better at our jobs, more efficient at business, and help save lives? Will it help us make faster decisions, better at solving problems, and more consistent in the outcomes?
What is AI?
First of all, let’s define what we mean by AI. The term artificial intelligence (AI) is a very broad topic and a large set of mathematical and software tools used to make machines seem smart. Therefore, it’s not fair to judge AI as a whole. While AI has been around for more than 50 years, recent significant advances in Machine Learning (ML) have brought AI back into the spotlight. Machine Learning is a very powerful tool in the AI toolbox, a concept that allows us to use data, and categorize that data to perform comparisons. The level of volume and accuracy in those comparisons is what has evolved considerably over the last few years.
I was recently at MIT’s EmTech Digital Conference in San Francisco, attended by many of the smartest companies in the world evolving AI and Machine Learning. These companies are bringing AI techniques out of research and academia and into the commercial space. Companies such as Google, Amazon, Baidu, IBM, Microsoft, and Sense Time, to name a few, are making huge strides in Machine Learning using Neural Networks to enhance the machine’s ability to model data, compare, and deliver results with great accuracy. This is called Deep Learning. Deep Learning was at the peak of the Gartner hype cycle in 2017, and this is part of the reason.
These same companies are providing capabilities to developers across the globe for commercial use, thus the reason for the hype explosion. Instantly, everyone thinks they can use “AI” to transform their business, enhance every decision support tool, and create automation to every problem they have. Some just want to use AI to say they’re using it. Crazy, but not unusual: Remember the “World Wide Web” in 1998, and iPhone apps in 2008? Businesses jumped on bandwagon even though they didn’t completely understand why.
According to Gallup, 85% of Americans are using some form of AI capability, with smart phones and voice assistants being the biggest examples.
Good or Bad
With the latest news about Cambridge Analytica and Facebook, people are becoming skeptical of basic data collection. Machine Learning relies on accurate data to work well; if it’s trained with garbage data or nefarious data, it will deliver those kinds of results. Remember Microsoft’s chatbot called Tay? It lasted less than 24 hours before it was shut down when Internet trolls were training it with data to say inappropriate things.
As with many technologies, Artificial Intelligence and Machine Learning can be used for good and bad purposes. Think about all the other technologies that fit that description: the internet, money, cloning, guns, cameras, and pesticides, for instance. We, as humans, need to understand that and treat AI with caution and respect. With other technologies, we don’t limit the tools, we govern the uses, and that same principle needs to be applied to how we use AI in the commercial space.
According to Gallup, 85% of Americans are using some form of AI capability, with smart phones and voice assistants being the biggest examples. Machine Learning has also transformed voice to text and natural language processing. With huge improvements over the last couple of years, we’re now able to see real-time translation from English to Chinese and vice versa, capabilies that Baidu showed off at EmTech with their pocket translator.
Smart speakers are bringing voice assistants into homes, and creating many useful applications for people who need daily assistance, including with healthcare; smart speakers are also providing entertainment and education applications for families and children.
Web technology platforms are using Machine Learning to deliver more relevant content to audiences. Adobe led the way with its announcement of Sensei in 2017, and other tech platforms have followed. Once businesses embrace this technology, it will help solve the problem of forcing consumers to sift through websites to find what they’re looking for. Consider this: Amazon figured out that relevant product recommendations were good for both the customer and businesses a long time ago.
While some are doing it now, we will soon start to see how advancements in Machine Learning technologies will make businesses more efficient, cost effective, and deliver better products. However, we need to be smart about how we approach using Machine Learning in business, or we’ll see an increase in spending for all the wrong reasons.
Machine learning can help us solve many problems that are impossible to solve with traditional software development and data analysis. Any time you can make this kind of leap forward in technical capability, solutions follow that were previously only dreamed about. Done right, AI, ML, and Deep Learning will allow us to build the next level of intelligent systems with potential to save lives, save energy, feed more people, and be more efficient as we explore areas of science that will help humanity and our planet thrive.
Hype vs Hope?
Is AI “our new bandwagon”? Think about it: chatbots, voice apps, intelligent systems – all applications of AI. But as with most hype cycles, there will be a trough of disillusionment. However, we can minimize the disappointment by being thoughtful. I think the answer is that we’ll see both hype and hope. While some won’t take the time to understand the real potential, Artifical Intelligence and Machine Learning are the next frontier of technology. They are the foundation for what’s next in the digital space – but it will take some time for us to learn how to use them right.
As a testament to his agnostic ability with technology and digital marketing, he has successfully launched a trifecta of Digital Experience Management platforms on each major technology stack, and created delivery practices around Adobe AEM, Sitecore, and most recently, Acquia.
Bret is a graduate of Metropolitan State University with a B.A., in Computer and Information Systems. He had over six years of military service with the United States Air Force and The Minnesota Air National Guard.